In the past, decisions frequently had to be made on minimal amounts of available data. Information traveled slowly, and the scope of the information available was within a scale that could be considered by a human mind. Frequently, the greatest problem facing a decision-maker was a paucity of information. Advances in information gathering and transmittal technologies have reversed this trend, making it easier to gather large amounts of information pertaining to a particular problem. A major task facing modern day decision-makers is filtering and organizing the received information into a useful form.
While automated classification and decision-making systems have become increasingly sophisticated, the human mind still outperforms automated systems on most real-world tasks. A limitation of human decision-making, however, is the inability of human beings simultaneously to consider a large number of factors. Decision-makers often find it difficult to combine mentally large amounts of evidence, since the human tendency is to postpone risky decisions when data are incomplete, jump to conclusions, or refuse to consider conflicting data. Accordingly, automated methods of organizing and displaying data can greatly aid human decision-makers.
In attempting to structure and filter the data presented to a human decision-maker, an unfortunate tendency of many automation systems intended to support decision-making is to oversimplify the situation presented to the decision-maker. While any real-world decision must include the consideration of many different types of uncertainty, this uncertainty is often hidden from the decision-maker within the automated system, leaving the user without explicit information regarding the uncertainty regarding each “fact” presented as relevant to the pending decision, which forces the decision-maker to guess about such uncertainty in arriving at a decision. Unfortunately, this can result in sub-optimal decisions, because vital information has in effect been hidden from the decision-maker by the automation system. A parallel situation pertains with regard to automated tools that perform analysis of a situation, and make either decisions or recommendations—current practice tends to “hide” the full range of interpretations of the input data, leading to inferior decisions and recommendations.